Time Series Analysis For The State Space Model With R Stan
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Time Series Analysis for the State-Space Model with R/Stan
Author | : Junichiro Hagiwara |
Publisher | : Springer Nature |
Total Pages | : 350 |
Release | : 2021-08-30 |
Genre | : Mathematics |
ISBN | : 9811607117 |
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This book provides a comprehensive and concrete illustration of time series analysis focusing on the state-space model, which has recently attracted increasing attention in a broad range of fields. The major feature of the book lies in its consistent Bayesian treatment regarding whole combinations of batch and sequential solutions for linear Gaussian and general state-space models: MCMC and Kalman/particle filter. The reader is given insight on flexible modeling in modern time series analysis. The main topics of the book deal with the state-space model, covering extensively, from introductory and exploratory methods to the latest advanced topics such as real-time structural change detection. Additionally, a practical exercise using R/Stan based on real data promotes understanding and enhances the reader’s analytical capability.
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